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Abstract

A program based on particle tracking velocimetry (PTV) was developed in this work. The
program was successfully validated by means of artificial images where parameters such as radius,
concentration, and noise were varied in order to test their influence on the results. This program
uses the mask cross correlation technique for particle centroid location. The sub-pixel accuracy is
achieved using two different methods, the three point Gaussian interpolation method and the center
of gravity method. The second method is only used if the first method fails. The object matching
algorithm between frames uses cross correlation with a non binarized image.
A performance comparison between different particle image velocimetry (PIV) and PTV algorithms
was done using the international standard PIV challenge artificial images. The best
performance was obtained by the program developed in this work. It showed the best accuracy,
and the best spatial resolution by finding the larger number of correct vectors of all algorithm
tested.
A procedure is proposed to obtain error estimates for real images based on errors calculated
with experimental ones. Using this procedure a real PIV image with 20% noise has an estimated
average error of 0.1 pixel.
Results of the analysis of 200 experimental images are shown for the two best PTV algorithms.